High-security biometric-based identification systems are fast evolving. Applications of these systems include identification of individuals for law enforcement purposes, limiting access to secure facilities, and information and verification of an individual's identity. These identification systems include the use of biometrics such as, fingerprints, voice analysis, retinal scans, iris scans, or the like.
Among different types of biometric measures, fingerprint has proven to be one of the most cost-effective methods for large-scale personal identification. One of the applications of the fingerprint recognition is fingerprint identification (i.e., a 1:N matching). A typical fingerprint identification process includes matching an input fingerprint (search fingerprint) to a database of previously collected fingerprint images to determine a person's identity.
A fingerprint image may originate from scanning of a finger, ink cards, from a palm, or latent prints. Different applications of fingerprint identification have different requirements. For example, the database size can range from hundreds of thousands to millions of images and the fingerprint images may vary in quality and size depending on their source.
Among many factors that may have an impact on the successful adoption of any Automatic Fingerprint Identification System (AFIS) in larger markets, system performance and cost are commonly considered as critical. A fingerprint database typically includes hundreds of thousands of fingerprint images from different sources. As the size of the database increases, the image quality and accuracy generated from the processing stage and the performance of the matching stage become critical to the overall system performance, that is, identification, accuracy, and response time of the system.
A major challenge of fingerprint identification systems is how to speed up the matching process without substantially compromising accuracy. To reduce the matching time, different approaches have been applied. For example, fingerprint classification, such as a four-class or five-class (whorl, left loop, right loop, arch and tented arch) scheme is commonly used to reduce the matched (candidate) list. For instance, a fingerprint with an arch does not need to be matched with those fingerprints consisting of whorls only. However, these classifications are not reliable in many cases and often cause misclassification. Another current approach is to utilize minutia information (i.e., discrete features on fingerprints) of the input fingerprint to reduce the number of matches. However, this minutia approach, while leading to a high degree of accuracy is substantially slow in processing.
Thus, seeking a better approach with high matching accuracy, high reduction rate, and short reduction time remains a challenge. Accordingly, there is a need for a scalable and configurable system for accommodating a variety of different applications for biometric matching and identification.